Design and implementation of non-linear guidance and control policies for dynamical systems that are robust to uncertainties in the initial and final state conditions, environmental parameters (e.g. pressure, temperature, obstacles, keep out zones, etc.), system parameters, path or route constraints and/or exogenous disturbances has previously been difficult due to ever present limitations on computational resources. Conventionally, a guidance and control policy is determined by optimizing a performance index, such as minimum effort, or minimum transition time assuming nominal initial conditions, system parameters and environmental conditions. A control policy designed in this manner is then typically subjected to a post-design Monte-Carlo analysis by selecting different possible operating conditions (e.g. initial conditions, parameter variations, etc.) to verify its effectiveness over the anticipated range of system uncertainties. A post-design Monte-Carlo analysis tests the ability of a nominal control policy to transition a perturbed dynamical system to the desired final state. A post-design Monte-Carlo analysis, however, is costly in terms of computation time. Moreover, the successful outcome of a Monte Carlo analysis depends on the properties of an initial nominal control policy and associated nominal system trajectory that is created previously, without regard to robustness. This cut and dry approach (i.e. design followed by post-design analysis and iteration) can lead to poor overall performance in the presence of uncertainties, and/or the need for multiple iterations before converging on a guidance and control policy having the desired behavior in the presence of uncertainties. Additionally, a guidance and control policy developed in this way may be overly conservative and prevent an otherwise high-performance dynamic system from being utilized to its full potential. Thus, to meet a given performance objective, the dynamical system may need to be overdesigned, which increases cost. Accordingly, improved techniques and apparatus are desirable for determining and implementing a guidance and control policy to transition a given dynamical system from an initial state to a desired final state in the presence of constraints and uncertainties regarding the initial and final states, the system parameters, environmental parameters and/or other disturbances.